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dc.contributor.authorJesang, Jean C.
dc.date.accessioned2021-04-27T09:51:28Z
dc.date.available2021-04-27T09:51:28Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/11071/10235
dc.descriptionA Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Statistical Sciences (MSc. SS) at Strathmore Universityen_US
dc.description.abstractBackground: Cervical cancer remains the second most commonly diagnosed cancer and the third leading cause of cancer death in developing countries. Improving clinicians' knowledge and understanding of surgical staging is critical in the fight against the disease. Kenya has limited research on accurately predicting the surgical stage following surgical treatment for cervical cancer. The uptake of predictive mechanisms by gynecologists has not been common. Objective: To assess prediction by comparing the odds ratios of three popular ordinal regression models i.e. the Multinomial Logistic Regression (MLR) model, the Continuation Ratio (CR) model and Adjacent Category Logistic (ACL) model when applying cervical cancer data in surgical stage prediction. Method: We systematically compared the performance of MLR, CR and the ACL as the predictive mechanisms and evaluated the most appropriate model in the cervical cancer setting. The study considered women who visited the Oncology department at the Moi Teaching and Referral Hospital's Chandaria Cancer and Chronic Diseases Center and were diagnosed and surgically treated for cervical cancer from January 2014 to December 2018. Results and conclusion: We presented the comparison between 3 different regression models for ordinal data within the cervical cancer setting. We choose to carry out an inferential and a predictive approach. The inferential approach found that the CR model without proportional odds yielded better results when comparing the Akaike Information Criterion (AIC), log likelihood ratio and residual deviance. In addition, the key prognostic factor associated with invasive cervical cancer was the FIGO clinical stage which in particular, had a higher influence on the surgical stage 2 outcomes compared to the lesser surgical stage categories. All the 5 independent features selected for classifying the patients into surgical stages were the FIGO clinical stage and partly, the presence or absence of cancer of symptomatic vaginal discharge. However, the predictive approach found that the MLR, CR and ACL models were not statistically different and not suitable for the prediction of the surgical stage among the women surgically treated for cervical cancer.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectSurgical stageen_US
dc.subjectOrdinal regressionen_US
dc.subjectCervical canceren_US
dc.subjectOdds ratioen_US
dc.subjectPredictive variablesen_US
dc.titleAssessing efficient odds ratios: an application to surgical stage prediction in cervical canceren_US
dc.typeThesisen_US


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